Smart grids (SGs) revolutionize existing power grids by using a wide range of developing disruptive technologies to generate clean, efficient, and predictable energy. Our study uses an action research method and focuses solely on the first two stages of the action research process, diagnosis and action planning, to evaluate ways to adopt artificial intelligence (AI) applications in SGs for predictive analytics in practice. The diagnosis stage of the study entails conducting a systematic literature review on AI applications in SGs, highlighting four areas of potential for predictive analytics: power outage prediction, demand response, control and coordination, and AI-enabled security to optimize decision-making, diagnose faults, and improve grid stability and security. The action planning step included a document analysis to devise methods to enable the practical implementation of AI in smart grids for predictive analytics. Finally, we address practical ways for implementing transparent AI for predictive analytics, followed by a conclusion and future research direction. The study’s key conclusion is that more research is needed to complete the action taking (implementing the solution), evaluation (assessing the results), and learning (reflecting on lessons learned) phases of the action research cycle.

The shift toward decentralised energy generation, driven by the rapid expansion of renewable energy sources, is redefining traditional consumers as active co-creators of electricity generation. This study examines the role of predictive analytics in supporting resilience and co-creation within distributed energy resources (DERs). It employs semi-structured qualitative interviews to explore stakeholder perspectives on how predictive analytics is envisioned and applied. Our preliminary findings show that predictive analytics could be used across diverse applications, such as forecasting, maintenance, and load optimisation. These diverse applications actively shape energy management systems while influencing decision-making and operational design. The application of predictive analytics is shaped by both enabling and constraining conditions. We conclude that predictive analytics functions not only as a technical tool but also aligns stakeholders and promotes collaborative and adaptive energy management among distributed energy resources.